amazon_us_reviews

Riferimenti:

• Codice
• Abbracciare la faccia

Libri_v1_01

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Books_v1_01')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Orologi_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Watches_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Personal_Care_Appliances_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Personal_Care_Appliances_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Mobile_Electronics_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Mobile_Electronics_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Digital_Video_Games_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Digital_Video_Games_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Digital_Software_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Digital_Software_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Major_Appliances_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Major_Appliances_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Carta_regalo_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Gift_Card_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Video_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Video_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Bagagli_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Luggage_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Software_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Software_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Video_Games_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Video_Games_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Mobili_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Furniture_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Strumenti_musicali_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Musical_Instruments_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Digital_Music_Purchase_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Digital_Music_Purchase_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Libri_v1_02

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Books_v1_02')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Home_Intrattenimento_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Home_Entertainment_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Drogheria_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Grocery_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

All'aperto_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Outdoors_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Pet_Products_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Pet_Products_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Video_DVD_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Video_DVD_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Abbigliamento_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Apparel_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

PC_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/PC_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Strumenti_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Tools_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Gioielli_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Jewelry_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Baby_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Baby_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Home_Miglioramento_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Home_Improvement_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Fotocamera_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Camera_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Prato_e_Giardino_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Lawn_and_Garden_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Office_Prodotti_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Office_Products_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Elettronica_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Electronics_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Automotive_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Automotive_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Digital_Video_Download_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Digital_Video_Download_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Mobile_Apps_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Mobile_Apps_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Scarpe_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Shoes_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Giocattoli_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Toys_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Sport_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Sports_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Cucina_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Kitchen_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Bellezza_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Beauty_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Musica_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Music_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Health_Personal_Care_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Health_Personal_Care_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Digital_Ebook_Purchase_v1_01

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Digital_Ebook_Purchase_v1_01')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Casa_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Home_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Wireless_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Wireless_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Libri_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Books_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

Digital_Ebook_Purchase_v1_00

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:amazon_us_reviews/Digital_Ebook_Purchase_v1_00')

• Descrizione :

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.

• Licenza : nessuna licenza conosciuta
• Versione : 0.1.0
• Divide :

• Caratteristiche :

{
    "marketplace": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "customer_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_parent": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "product_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "star_rating": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "helpful_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "total_votes": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "vine": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "verified_purchase": {
        "num_classes": 2,
        "names": [
            "N",
            "Y"
        ],
        "id": null,
        "_type": "ClassLabel"
    },
    "review_headline": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_body": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "review_date": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}